ConceptPose delivers state-of-the-art zero-shot relative pose estimation by matching open-vocabulary 3D concept vectors derived from VLM saliency maps, beating the strongest baseline by 62% in ADD(-S) without training.
hub
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
13 Pith papers cite this work. Polarity classification is still indexing.
13
Pith papers citing it
hub tools
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
Tracker is a self-supervised VL tracker that uses a Dynamic Token Aggregation Module to learn instance tracking from single language descriptions in unlabeled videos and outperforms prior self-supervised methods.
PolySLGen generates contextually appropriate and temporally coherent multimodal speaking and listening reactions for polyadic interactions by fusing group motion and social cues.
PLUME uses latent-state autoregressive rollouts and a progressive training curriculum to deliver efficient reasoning for universal multimodal embeddings without generating explicit rationales.
AV-SpeakerBench is a new speaker-centered benchmark showing that top multimodal models still struggle with fine-grained audiovisual speech understanding, with Gemini 2.5 Pro leading but open models lagging on fusion.
Vision Inference Former adds a direct visual-to-output bridge that continuously injects visual semantics during MLLM decoding to sustain consistency and reduce modality imbalance.
GLMap combines explicit 3D Gaussians with multi-scale language semantics in a dual-modality structure and uses an analytical Gaussian Estimator for incremental map building, improving zero-shot performance on navigation and reasoning tasks.
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
G-MIXER achieves state-of-the-art zero-shot composed image retrieval by using geodesic mixup to build diverse implicit candidates and MLLM-derived explicit semantics for re-ranking.
SpatialStack improves 3D spatial reasoning in vision-language models by stacking and synchronizing multi-level geometric features with the language backbone.
Nano-EmoX is a compact 2.2B multimodal model that unifies six core affective tasks across perception, understanding, and interaction levels via a curriculum framework, achieving competitive benchmark performance.
MedBridge adapts pretrained VLMs to multi-label medical diagnosis via query tokens for non-destructive alignment and expert routing, reporting 6-15% AUC gains on chest radiograph benchmarks across eight models.
citing papers explorer
-
ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors
ConceptPose delivers state-of-the-art zero-shot relative pose estimation by matching open-vocabulary 3D concept vectors derived from VLM saliency maps, beating the strongest baseline by 62% in ADD(-S) without training.
-
Learning to Track Instance from Single Nature Language Description
Tracker is a self-supervised VL tracker that uses a Dynamic Token Aggregation Module to learn instance tracking from single language descriptions in unlabeled videos and outperforms prior self-supervised methods.
-
PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction
PolySLGen generates contextually appropriate and temporally coherent multimodal speaking and listening reactions for polyadic interactions by fusing group motion and social cues.
-
PLUME: Latent Reasoning Based Universal Multimodal Embedding
PLUME uses latent-state autoregressive rollouts and a progressive training curriculum to deliver efficient reasoning for universal multimodal embeddings without generating explicit rationales.
-
See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models
AV-SpeakerBench is a new speaker-centered benchmark showing that top multimodal models still struggle with fine-grained audiovisual speech understanding, with Gemini 2.5 Pro leading but open models lagging on fusion.
-
Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
Vision Inference Former adds a direct visual-to-output bridge that continuously injects visual semantics during MLLM decoding to sustain consistency and reduce modality imbalance.
-
Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning
GLMap combines explicit 3D Gaussians with multi-scale language semantics in a dual-modality structure and uses an analytical Gaussian Estimator for incremental map building, improving zero-shot performance on navigation and reasoning tasks.
-
CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging
CheXmix combines masked autoencoder pretraining with early-fusion generative modeling to outperform prior models on chest X-ray classification by up to 8.6% AUROC, inpainting by 51%, and report generation by 45% on GREEN.
-
G-MIXER: Geodesic Mixup-based Implicit Semantic Expansion and Explicit Semantic Re-ranking for Zero-Shot Composed Image Retrieval
G-MIXER achieves state-of-the-art zero-shot composed image retrieval by using geodesic mixup to build diverse implicit candidates and MLLM-derived explicit semantics for re-ranking.
-
SpatialStack: Layered Geometry-Language Fusion for 3D VLM Spatial Reasoning
SpatialStack improves 3D spatial reasoning in vision-language models by stacking and synchronizing multi-level geometric features with the language backbone.
-
Nano-EmoX: Unifying Multimodal Emotional Intelligence from Perception to Empathy
Nano-EmoX is a compact 2.2B multimodal model that unifies six core affective tasks across perception, understanding, and interaction levels via a curriculum framework, achieving competitive benchmark performance.
-
Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging
MedBridge adapts pretrained VLMs to multi-label medical diagnosis via query tokens for non-destructive alignment and expert routing, reporting 6-15% AUC gains on chest radiograph benchmarks across eight models.
- A Geometric Algebra-Informed 3DGS Framework for Wireless Channel Prediction